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Poster abstract: EIL: an environment-independent device-free passive localization approach

Authors:

Ju Wang,

Tianzhang XingAuthors Info & Claims

IPSN '14: Proceedings of the 13th international symposium on Information processing in sensor networks

Pages 291 - 292

Published: 15 April 2014 Publication History

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Abstract

Most previous Device-free Passive Localization (DFL) methods are learning based and they assume the distribution of Received Radio Signal (RSS) distorted by an object is fixed across time. However, the signals significantly vary over time and the pre-obtained radio map (or prior knowledge) outdated in the localization phase, thus causing the localization accuracy decrease. To cope with this problem, this poster proposes, EIL, an environment-independent DFL approach which can improve the system robustness and localization accuracy by eliminating the interference of environment on RSS over time in both the training phase and the localization phase. Through both the extensive experiments and simulations, EIL keeps a range of 0.5m to 0.6m localization errors for 90% locations over time.

References

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D. Zhang, Y. Liu, and L. M. Ni, ''Rass: A real-time, accurate and scalable system for tracking transceiver-free objects,'' in PerCom'11, pp. 197--204, IEEE, 2011.

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Index Terms

Poster abstract: EIL: an environment-independent device-free passive localization approach
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation theory
      1. Systems theory
2. Mathematics of computing
  1. Information theory

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Information

Published In

IPSN '14: Proceedings of the 13th international symposium on Information processing in sensor networks

April 2014

368 pages

ISBN:9781479931460

General Chair:
Adam Wolisz
TU Berlin, Germany
,
Program Chairs:
Jie Liu
Microsoft Research, USA
,
Lin Zhong
Rice University, USA

Publisher

IEEE Press

Publication History

Published: 15 April 2014

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IPSN '14 Paper Acceptance Rate 23 of 111 submissions, 21%;

Overall Acceptance Rate 143 of 593 submissions, 24%

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Index Terms

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Poster abstract: Implications of target diversity for organic device-free localization

Poster abstract: NDP: a novel device-free localization method with little efforts

High accurate localisation using multipath effects via physical layer information